On Wed, Jan 4, 2012 at 12:35 AM, Travis Oliphant <travis@continuum.io> wrote:
>> On Jan 3, 2012, at 9:44 PM, josef.pktd@gmail.com wrote:
>> I did a bit of a literature search on splines, mainly to get some
> overview on its use in statistics.
>> There are quite a few different versions of splines and I don't have
> too much of an idea which is which.
>>>> Does anyone now a good reference that gives an overview of different
> splines? (So far I only know b-splines since they are in scipy.)
>>> This book by De Boor is a standard.
>http://books.google.com/books?printsec=frontcover&id=m0QDJvBI_ecC#v=onepage&q&f=false> I remember using it 5 years ago when I added some low-level
> spline-calculation pieces to interpolate.
Thanks, I'm going to look at it.
>>> I don't really want to get into the gory details of splines, but I
> would like to have a collection of basis functions for different
> splines, similar to the polynomials vander functions that Chuck added
> or is adding in numpy. Just something to feed to a (penalized) least
> squares estimation.
>>> semi-aside: I just saw for the first time a reference that uses a
> polynomial up to a fixed order and then adds spline terms, which looks
> like an interesting combination of polynomial and (piecewise) spline
> fitting.
>>> That sounds interesting. It sounds like fitting a general trend to the
> data and then using splines to fit the difference between the data and the
> "trend". Do you have the reference handy?
David Ruppert, “Selecting the Number of Knots for Penalized Splines,”
Journal of Computational and Graphical Statistics 11, no. 4 (December
1, 2002): 735-757.
http://scholar.google.com/scholar?cluster=3369320305950511369&hl=en&as_sdt=0,5
I just skimmed it, they also refer to Hastie and Tibishirani which I
haven't looked at in a while.
Josef
>> -Travis
>>>> Thanks,
>> Josef
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